Preliminary observations of the SELENE Gamma Ray Spectrometer

Details Preliminary observations of the SELENE Gamma Ray Spectrometer Preliminary observations of the SELENE Gamma Ray Spectrometer

Sep 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008epsc.conf..229f&link_type=abstract

European Planetary Science Congress 2008, Proceedings of the conference held 21-25 September, 2008 in Münster, Germany. Online a

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Introduction We analyze the spectra measured by the Gamma Ray Spectrometer (GRS) on board the SELENE satellite [1]. SELENE was inserted in lunar orbit on 4 Oct. 2007. After passing through a health check and a function check, the GRS was shifted to nominal observation on 21 Dec. 2007. The spectra consist in various lines of interest (O, Mg, Al, Si, Ti, Ca, Fe, K, Th, U, and possibly H) superposed on a continuum. The energies of the gamma rays identify the nuclides responsible for the gamma ray emission and their intensities relate to their abundance. Data collected through 17 Feb. 2008 are studied here, corresponding to an accumulation time (Fig. 1) sufficiently good to allow preliminary mapping. Analysis of the global gamma ray spectrum In order to obtain spectra with counting statistics sufficient for peak analysis, we accumulate all observations. The identification of lines is performed on this global lunar spectrum (Fig 2). Fit of individual lines The gamma ray lines that arise from decay of longlived radioactive species are among the easiest to analyze. So far the abundance of two species is studied thanks to such lines: potassium (1461 keV) and thorium (2614 keV). Secondary neutrons from cosmic ray interactions also produce gamma ray when reacting with the planetary material, according to scattering or absorption reactions. However these lines need substantial corrections before an interpretation in terms of abundance can be performed. Lines have been examined with different techniques. The simplest method consists in summing the spectra in a window containing the line of interest. The continuum is adjusted with a polynomial and removed. Such a method was used for the gamma ray spectra collected by Lunar Prospector [2]. This method is especially robust for isolated lines, such as those of K and Th mentioned above, or with very low statistics. The second method consists in fitting the lines by summing a quadratic continuum with Gaussian lines and exponential tails. We presently fit the spectra thanks to a program developed at CESR: Aquarius. Afterwards the areas associated with the parameters of these ideal lines are calculated. This method is welladapted for interfering lines, such as U, Al, and H around 2210 keV, but it requires good statistics. These two methods were used to analyze the Mars Odyssey gamma-ray spectra [3]. Prettyman et al. [4] applied a third method where theoretical spectra are simulated and matched against the observations. Below we propose a fourth approach based on statistical analyzes. Mapping of elemental abundances Data returned by the spacecraft are time-tagged records acquired with a resolution of 17 seconds. The angular distance covered by the spacecraft during this interval corresponds to about 1° at the surface. However the true resolution of the instrument is lower because gamma rays come from all directions onto the spacecraft. The resolution is therefore set by the field of view of the instrument, which depends on the spacecraft altitude and the geometry of the instrument. The full width half maximum of the instrumental response has been estimated to be 130 km at 1 MeV by the SELENE GRS team. We have tiled the data in agreement with the better resolution we could obtain depending on the intensity of a given line. The thorium line at 2614 keV was thus mapped at a resolution of 3° with the first method described above (sum over 2550-2640 keV). Then this map was smoothed with a 5° filter (152 km radius) to approximate the response function of the instrument. Finally the counting rate was converted into abundance (Fig. 3), using the compositions at landing sites and in the highlands as did Gillis et al. [5]. Statistical analysis We have also analysed the data with various multivariate techniques, one of them being the Independent Component Analysis (ICA) [6, 7]. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed non- Gaussian and mutually independent and they are called the independent components of the observed data. These independent components, also called sources or factors, can be found by ICA. This is done by maximising a non-gaussianity criterion of the sources. As in [8], we have used the JADE algorithm developed and described in [9] for our analysis that we focused in the energy range from 750 to 3000 keV. We identify at least three meaningful components. The first one is correlated to the Thorium map (Fig. 4). The corresponding correlation coefficient spectrum exhibits the lines of Thorium, Potassium and Uranium (Fig. 5). The second component (Fig. 6) is clearly correlated with the Iron as shown on its corresponding spectrum (Fig. 5). A third component, identified at lower resolution, seems to be partly correlated with the altitude of the spacecraft (not shown). Further improvement in the data reduction, like corrections for altitude, cosmic ray, and neutron current variations should allow a better interpretation of the data. Acknowledgement. The SELENE GRS team members are: N. Hasebe, O. Okudaira, N. Yamashita, S. Kobayashi, Y. Karouji, M. Hareyama, S. Kodaira, S. Komatsu, K. Hayatsu, K. Iwabuchi, S. Nemoto, E. Shibamura, M.-N. Kobayashi, R.C. Reedy, K.J. Kim, C. d'Uston, S. Maurice, O. Gasnault, O. Forni, B. Diez. References. [1] Hasebe, N. et al. (2008) Earth, Planets and Space, 60, 299-312.. [2] Lawrence, D.J. et al. (1999) Geophys. Res. Lett., 26 (17), 2681-2684. [3] Evans, L.E. et al. (2006) J. Geophys. Res., 111, E03S04. [4] Prettyman, T.H. et al. (2006) J. Geophys. Res., 111, E12007. [5] Gillis, J.J. et al. (2004) Geo. et Cosmo. Acta, 68 (18), 3791-3805. [6] Comon P. (1994) Signal Processing, 36, 287-314. [7] Hyvärinen, A. and E. Oja (2000) Neural Networks, 13(4-5), 411-430. [8] Forni O. et al. (2005) LPSC, 36, 1623 [9] Cardoso, J.-F. (1997) IEEE Letters on Signal Processing, 4, 112-114.

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